Field of the Invention
The invention relates generally to the field of image processing, and more particularly relates to automated optical inspection, template matching segmentation, classification, features extraction, pattern recognition techniques and machine learning of digitized microscope slides.
The invention relates also to histology analyses, and in particular, to decision support systems for diagnostic use by pathology professionals for the determination of the presence of bacteria in tissue biopsies, in general, and presence of Helicobacter pylori (“HP”) in histological specimens of the stomach, in particular.
Description of the Related Art
Histology is a branch of science which studies the microscopic structures of organic tissues. Histological analysis is performed by examining a thin slice of a microscopically sectioned tissue biopsy under a microscope, in order to determine the possible presence of cells of a certain cellular type, or to recognize a variety of microbial components or to recognize architectural structures of the tissue. For example, a histological analysis may be performed on a tissue sample slide to detect the presence of HP in a tissue biopsy.
HP is one of the most common bacterial pathogens; it infects the gastric mucosa in humans and it is estimated that by adulthood over half the world population is infected. This bacterium is Gram negative, spiral-shaped, and motile by polar flagella.
A certain proportion of the infected population will develop acute gastritis or gastric or duodenal ulcers. Some persons may develop gastric cancers. Because of strong association between gastric cancer and HP infection this bacterium is classified as a bacterial carcinogen.
Gastric biopsies are often extracted during endoscopic examination to rule out HP infection by histologic evaluation. Histology is considered to be the reference (“gold standard”) in the direct diagnosis of HP gastritis. Before observation, slices are subjected to staining in order to obtain the prepared pieces to be inspected and enhance contrast. An experienced pathologist is needed to inspect the histological slice in order to determine the possible presence of HP. This procedure is exhausting and prone to a high false negative rate due to the tedium and fatigue suffered by the pathologist.
Some attempts in the field of automated image segmentation and classification of histological specimens have been made, based on morphometric and color features, but have encountered difficulties due to the unique characteristics of histology imaging and the complex structures associated with cells and tissue architecture.
Identification of components in an image requires applying segmentation techniques for classification. The slide specimen includes many complex arrangements and architectures, including overlapping tissue components, cell boundaries and nuclei corrupted by noise which is not present in a blood sample for example. Some structures, such as cells, may appear connected, blurred or occluded by other tissue elements in the image. Furthermore, the cell architecture and bacteria may be presented in various 3D orientations due to the method of slicing that exacerbate the challenge of image analysis. Those complexities have made histological slides difficult to extract cell regions and bacteria by traditional image segmentation approaches. When attempting to segment and recognize bacteria in tissue all the above challenges exist and are made significantly more complicated because of the small size of bacteria in histological images both in absolute measure and by their much smaller size relative to the surrounding tissue features. Identification of the boundaries of a bacterium is especially difficult on such background.
The concept of a “neural network,” per se, is known in the art, disclosed for example in U.S. Pat. Nos. 5,287,272; 6,327,377; and 8,655,035 which are incorporated herein by reference in their entirety for their teaching of image analysis techniques known in the art. However, a convolutional neural network (“CNN”) or similar machine learning algorithm has not previously been applied to the identification of one or more bacteria in a histological specimen, mainly because the small size of bacteria coupled with the background “noise” in the image impedes automated identification. Teaching a machine to identify HP in tissue through an iterative process of machine learning represents a novel contribution to the state of the art.